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Background: Pleural Mesothelioma (PM) is an unusual, belligerent tumor that
rapidly develops into cancer in the pleura of the lungs. Pleural Mesothelioma
is a common type of Mesothelioma that accounts for about 75% of all
Mesothelioma diagnosed yearly in the U.S. Diagnosis of Mesothelioma takes
several months and is expensive. Given the risk and constraints associated with
PM diagnosis, early identification of this ailment is essential for patient
health. Objective: In this study, we use artificial intelligence algorithms
recommending the best fit model for early diagnosis and prognosis of MPM.
Methods: We retrospectively retrieved patients clinical data collected by Dicle
University, Turkey, and applied multilayered perceptron (MLP), voted perceptron
(VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic
gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and
primal estimated sub-gradient solver for support vector machine (s-Pegasos). We
evaluated the models, compared and tested using paired T-test (corrected) at
0.05 significance based on their respective classification accuracy, f-measure,
precision, recall, root mean squared error, receivers characteristic curve
(ROC), and precision-recall curve (PRC). Results: In phase-1, SGD, AdaBoost.
M1, KLR, MLP, VFDT generate optimal results with the highest possible
performance measures. In phase 2, AdaBoost, with a classification accuracy of
71.29%, outperformed all other algorithms. C-reactive protein, platelet count,
duration of symptoms, gender, and pleural protein were found to be the most
relevant predictors that can prognosticate Mesothelioma. Conclusion: This study
confirms that data obtained from Biopsy and imagining tests are strong
predictors of Mesothelioma but are associated with a high cost; however, they
can identify Mesothelioma with optimal accuracy.